Identification Of Objects By Machines Using RFID Technology Identify objetcs in Internet of Things

Authors

  • Douae Salim Advanced Technology laboratory Faculty of science Abdelmalek Essaâdi University Larache, Morocco
  • Larbi Setti Advanced Technology laboratory Department of Physics, Multi-disciplinary faculty, Abdelmalek Essaâdi University, Larache, Morocco
  • Abdellatif Elabderrahm Advanced Technology laboratory Department of Computer science Multi-disciplinary faculty Abdelmalek Essaâdi University, Larache, Morocco

DOI:

https://doi.org/10.14738/tmlai.54.3202

Keywords:

Internet of things, embedded systems, RFID, Tags, reader, radio-frequency.

Abstract

Embedded Systems and Internet of Things know recently a revolution in terms of innovation. With connectivity and networks, embedded systems are becoming more communicative, intelligent, and autonomous. They had the ability to calculate, process information, and perception. Digital connectivity to physical objects requires, firstly, the identification of objects, in order to be able to recognize each object in a unique way and to collect the data stored, ensuring security and confidentiality. In this paper we will examine the RFID technology (Radio-frequency Identification), which enables wireless interaction over certain frequency of RFID readers with a network system, to uniquely identify, track and capture informations at varying distances without the need of human interaction. Typically, RFID readers, which emits electromagnetic waves at a certain power, in order to activate the RFID tags, if it presents in the reader’s transmission range. RFID tags respond to the reader’s request by emitting radio waves back with the data stored the chip. Furthermore, we will explore Identification algorithm and communication techniques between reader and tags to avoid collision, whenever a large volume of tags must be read together, or perform a transmission in the same RF field at the same time. In addition, we will describe communications protocol between reader and the information system.

References

(1) Jae-Ryong Cha and Jae-Hyun Kim, “Dynamic framed slotted ALOHA algorithms using fast tag estimation method for RFID system,” 2006, vol. 2, pp. 768–772.

(2) H. Safa, W. El-Hajj, and C. Meguerditchian, “A distributed multi-channel reader anti-collision algorithm for RFID environments,” Comput. Commun., vol. 64, pp. 44–56, Jun. 2015.

(3) M. He et al., “A fast RFID tag identification algorithm based on counter and stack,” Expert Syst. Appl., vol. 38, no. 6, pp. 6829–6838, Jun. 2011.

(4) Hamid Jabbar and Taikyeong Ted. Jeong, RFID System Integration. INTECH Open Access Publisher, 2010.

(5) I. SZENTMIKLÓSI and B. ILLÉS, “DESIGN, OPERATION AND ANTI-COLLISION PROCEDURE OF DATA TRANSFER OF IN℡LIGENT LOAD UNITS.”

(6) H.-W. Wang, “Efficient DFSA Algorithm in RFID Systems for the Internet of Things,” Mob. Inf. Syst., vol. 2015, pp. 1–10, 2015.

(7) A. Thomas, “RFID et nouvelles technologies de communication; enjeux économiques incontournables et problèmes d’éthique,” in 6ème Conférence Internationale Conception et Production Integrées, CPI’2009, 2009.

(8) J. Myung, W. Lee, and J. Srivastava, “Adaptive binary splitting for efficient RFID tag anti-collision,” IEEE Commun. Lett., vol. 10, no. 3, pp. 144–146, 2006.

(9) X. Yi, L. Wang, D. Mao, and Y. Zhan, “An Gen2 Based Security Authentication Protocol for RFID System,” Phys. Procedia, vol. 24, pp. 1385–1391, 2012.

(10) http://www.enigmaticconsulting.com/Communications_articles/RFID/RFID_protocols.html

(11) https://www.etilux.be/fr/services/identification-etiquetage-marquage/tracabilite-rfid.php

(12) http://rfid4u.com/rfid-basics-resources/epc-gen2-reader-commands-and-q-parameter/

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Published

2017-09-01

How to Cite

Salim, D., Setti, L., & Elabderrahm, A. (2017). Identification Of Objects By Machines Using RFID Technology Identify objetcs in Internet of Things. Transactions on Engineering and Computing Sciences, 5(4). https://doi.org/10.14738/tmlai.54.3202

Issue

Section

Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems